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The authors attempted to validate a 2-stage strategy to screen for severe obstructive sleep apnea syndrome (s-OSAS) among hypertensive outpatients, with polysomnography (PSG) as the gold standard. Using a prospective design, outpatients with hypertension were recruited from medical outpatient clinics. Interventions included (1) assessment of clinical data; (2) home sleep testing (HST); and (3) 12-channnel, in-laboratory PSG. The authors developed models using clinical or HST data alone (single-stage models) or clinical data in tandem with HST (2-stage models) to predict s-OSAS. For each model, area under receiver operating characteristic curves (AUCs), sensitivity, specificity, negative likelihood ratio, and negative post-test probability (NPTP) were computed. Models were then rank-ordered based on AUC values and NPTP. HST used alone had limited accuracy (AUC=0.727, NPTP=2.9%). However, models that used clinical data in tandem with HST were more accurate in identifying s-OSAS, with lower NPTP: (1) facial morphometrics (AUC=0.816, NPTP=0.6%); (2) neck circumference (AUC=0.803, NPTP=1.7%); and Multivariable Apnea Prediction Score (AUC=0.799, NPTP=1.5%) where sensitivity, specificity, and NPTP were evaluated at optimal thresholds. Therefore, HST combined with clinical data can be useful in identifying s-OSAS in hypertensive outpatients, without incurring greater cost and patient burden associated with in-laboratory PSG. These models were less useful in identifying obstructive sleep apnea syndrome of any severity.